Overview

Dataset statistics

Number of variables39
Number of observations189588
Missing cells1042065
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory281.7 MiB
Average record size in memory1.5 KiB

Variable types

CAT23
NUM14
UNSUPPORTED1
BOOL1

Reproduction

Analysis started2020-09-03 08:08:03.422423
Analysis finished2020-09-03 08:09:04.731832
Duration1 minute and 1.31 second
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

REPORTNO has a high cardinality: 189585 distinct values High cardinality
LOCATION has a high cardinality: 23951 distinct values High cardinality
INCDATE has a high cardinality: 5948 distinct values High cardinality
INCDTTM has a high cardinality: 160155 distinct values High cardinality
ST_COLDESC has a high cardinality: 61 distinct values High cardinality
OBJECTID is highly correlated with df_index and 3 other fieldsHigh correlation
df_index is highly correlated with OBJECTID and 3 other fieldsHigh correlation
INCKEY is highly correlated with df_index and 3 other fieldsHigh correlation
COLDETKEY is highly correlated with df_index and 3 other fieldsHigh correlation
SEVERITYCODE.1 is highly correlated with SEVERITYCODEHigh correlation
SEVERITYCODE is highly correlated with SEVERITYCODE.1High correlation
SDOTCOLNUM is highly correlated with df_index and 3 other fieldsHigh correlation
SEVERITYCODE.1 is highly correlated with SEVERITYCODE and 1 other fieldsHigh correlation
SEVERITYCODE is highly correlated with SEVERITYCODE.1 and 1 other fieldsHigh correlation
SEVERITYDESC is highly correlated with SEVERITYCODE and 1 other fieldsHigh correlation
ST_COLDESC is highly correlated with COLLISIONTYPEHigh correlation
COLLISIONTYPE is highly correlated with ST_COLDESCHigh correlation
X has 5186 (2.7%) missing values Missing
Y has 5186 (2.7%) missing values Missing
INTKEY has 126118 (66.5%) missing values Missing
LOCATION has 2565 (1.4%) missing values Missing
EXCEPTRSNCODE has 109777 (57.9%) missing values Missing
EXCEPTRSNDESC has 184725 (97.4%) missing values Missing
JUNCTIONTYPE has 6176 (3.3%) missing values Missing
INATTENTIONIND has 159800 (84.3%) missing values Missing
PEDROWNOTGRNT has 184954 (97.6%) missing values Missing
SDOTCOLNUM has 74837 (39.5%) missing values Missing
SPEEDING has 180262 (95.1%) missing values Missing
SEGLANEKEY is highly skewed (γ1 = 69.22763158) Skewed
REPORTNO is uniformly distributed Uniform
df_index has unique values Unique
OBJECTID has unique values Unique
INCKEY has unique values Unique
COLDETKEY has unique values Unique
ST_COLCODE is an unsupported type, check if it needs cleaning or further analysis Unsupported
PERSONCOUNT has 5536 (2.9%) zeros Zeros
PEDCOUNT has 182730 (96.4%) zeros Zeros
SDOT_COLCODE has 8972 (4.7%) zeros Zeros
SEGLANEKEY has 186973 (98.6%) zeros Zeros
CROSSWALKKEY has 185866 (98.0%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count189588
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95859.01714243517
Minimum0
Maximum194672
Zeros1
Zeros (%)< 0.1%
Memory size1.4 MiB
2020-09-03T16:09:04.829309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9625.35
Q147564.75
median95013.5
Q3143958.25
95-th percentile184307.65
Maximum194672
Range194672
Interquartile range (IQR)96393.5

Descriptive statistics

Standard deviation55842.22777
Coefficient of variation (CV)0.5825453821
Kurtosis-1.189440693
Mean95859.01714
Median Absolute Deviation (MAD)48194.5
Skewness0.03123344521
Sum1.817371934e+10
Variance3118354402
2020-09-03T16:09:04.907996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
1611241< 0.1%
 
1406541< 0.1%
 
1447481< 0.1%
 
1345071< 0.1%
 
1324581< 0.1%
 
1386011< 0.1%
 
1365521< 0.1%
 
1590791< 0.1%
 
1570301< 0.1%
 
1631731< 0.1%
 
1508831< 0.1%
 
710481< 0.1%
 
1488341< 0.1%
 
1549771< 0.1%
 
1529281< 0.1%
 
443511< 0.1%
 
423021< 0.1%
 
484451< 0.1%
 
463961< 0.1%
 
361551< 0.1%
 
341061< 0.1%
 
1427031< 0.1%
 
1857121< 0.1%
 
1877611< 0.1%
 
Other values (189563)189563> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
1946721< 0.1%
 
1946711< 0.1%
 
1946701< 0.1%
 
1946691< 0.1%
 
1946681< 0.1%
 
1946671< 0.1%
 
1946661< 0.1%
 
1946651< 0.1%
 
1946641< 0.1%
 
1946631< 0.1%
 

SEVERITYCODE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
132627
2
56961
ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 
2020-09-03T16:09:05.634104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number189588100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common189588100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII189588100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

X
Real number (ℝ)

MISSING

Distinct count23414
Unique (%)12.7%
Missing5186
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean-122.33050451703778
Minimum-122.41909109999999
Maximum-122.2389494
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:05.716735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-122.4190911
5-th percentile-122.3830827
Q1-122.3488589
median-122.3301677
Q3-122.3119178
95-th percentile-122.2797559
Maximum-122.2389494
Range0.1801417
Interquartile range (IQR)0.0369411

Descriptive statistics

Standard deviation0.03002633681
Coefficient of variation (CV)-0.0002454525707
Kurtosis-0.2528954437
Mean-122.3305045
Median Absolute Deviation (MAD)0.0184227
Skewness-0.0599472372
Sum-22557989.69
Variance0.000901580902
2020-09-03T16:09:05.797350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-122.33265332600.1%
 
-122.34489612470.1%
 
-122.32807862420.1%
 
-122.34499682350.1%
 
-122.29915972290.1%
 
-122.35113392090.1%
 
-122.34729431820.1%
 
-122.34586311630.1%
 
-122.33245131570.1%
 
-122.26998791510.1%
 
-122.31094941440.1%
 
-122.32904871430.1%
 
-122.28992291400.1%
 
-122.33466561350.1%
 
-122.33917361350.1%
 
-122.32192041340.1%
 
-122.3299741300.1%
 
-122.32461521300.1%
 
-122.33557131290.1%
 
-122.2699821280.1%
 
-122.27766411260.1%
 
-122.31673341260.1%
 
-122.3023291260.1%
 
-122.33943911250.1%
 
-122.33375681240.1%
 
Other values (23389)18035295.1%
 
(Missing)51862.7%
 
ValueCountFrequency (%) 
-122.41909111< 0.1%
 
-122.419031814< 0.1%
 
-122.41897251< 0.1%
 
-122.41875741< 0.1%
 
-122.41861537< 0.1%
 
-122.41813951< 0.1%
 
-122.4181212< 0.1%
 
-122.41711381< 0.1%
 
-122.41711298< 0.1%
 
-122.41705481< 0.1%
 
ValueCountFrequency (%) 
-122.238949438< 0.1%
 
-122.23978061< 0.1%
 
-122.24108214< 0.1%
 
-122.24108843< 0.1%
 
-122.24112072< 0.1%
 
-122.24114511< 0.1%
 
-122.24139234< 0.1%
 
-122.24140214< 0.1%
 
-122.24141425< 0.1%
 
-122.24199544< 0.1%
 

Y
Real number (ℝ≥0)

MISSING

Distinct count23688
Unique (%)12.8%
Missing5186
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean47.619521035482926
Minimum47.49557292
Maximum47.73414158
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:05.885125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum47.49557292
5-th percentile47.52704457
Q147.57587276
median47.61535695
Q347.66383534
95-th percentile47.71506588
Maximum47.73414158
Range0.23856866
Interquartile range (IQR)0.08796258

Descriptive statistics

Standard deviation0.05623011516
Coefficient of variation (CV)0.001180820679
Kurtosis-0.820754523
Mean47.61952104
Median Absolute Deviation (MAD)0.04516837
Skewness0.06197564275
Sum8781134.918
Variance0.003161825851
2020-09-03T16:09:05.957189image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
47.70865452600.1%
 
47.71717312470.1%
 
47.604161232420.1%
 
47.725035552350.1%
 
47.579673462290.1%
 
47.570941782090.1%
 
47.647172491820.1%
 
47.612990811610.1%
 
47.607266311570.1%
 
47.522815641510.1%
 
47.595116281440.1%
 
47.568882381400.1%
 
47.609685421350.1%
 
47.613727071350.1%
 
47.708585791340.1%
 
47.70860281300.1%
 
47.521783111290.1%
 
47.524739041280.1%
 
47.551176021260.1%
 
47.654995231260.1%
 
47.612889241260.1%
 
47.60869261240.1%
 
47.608324561230.1%
 
47.708637431200.1%
 
47.549190591200.1%
 
Other values (23663)18038995.1%
 
(Missing)51862.7%
 
ValueCountFrequency (%) 
47.495572921< 0.1%
 
47.495806672< 0.1%
 
47.495892661< 0.1%
 
47.4959893710< 0.1%
 
47.496251116< 0.1%
 
47.496402958< 0.1%
 
47.496485712< 0.1%
 
47.496503614< 0.1%
 
47.496512852< 0.1%
 
47.496664791< 0.1%
 
ValueCountFrequency (%) 
47.734141585< 0.1%
 
47.734140592< 0.1%
 
47.734138912< 0.1%
 
47.734136563< 0.1%
 
47.7341361325< 0.1%
 
47.734135763< 0.1%
 
47.734135554< 0.1%
 
47.734135341< 0.1%
 
47.734134967< 0.1%
 
47.7341345811< 0.1%
 

OBJECTID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count189588
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106817.2209791759
Minimum1
Maximum219547
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:06.084960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12094.05
Q153116.75
median104253.5
Q3160018.25
95-th percentile207273.65
Maximum219547
Range219546
Interquartile range (IQR)106901.5

Descriptive statistics

Standard deviation62222.44327
Coefficient of variation (CV)0.5825132193
Kurtosis-1.176062245
Mean106817.221
Median Absolute Deviation (MAD)53440.5
Skewness0.07839980635
Sum2.025126329e+10
Variance3871632446
2020-09-03T16:09:06.156300image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
11941< 0.1%
 
585501< 0.1%
 
646931< 0.1%
 
626441< 0.1%
 
524031< 0.1%
 
503541< 0.1%
 
564971< 0.1%
 
544481< 0.1%
 
155331< 0.1%
 
134841< 0.1%
 
73371< 0.1%
 
1446201< 0.1%
 
52881< 0.1%
 
278151< 0.1%
 
257661< 0.1%
 
319091< 0.1%
 
298601< 0.1%
 
175701< 0.1%
 
237131< 0.1%
 
216641< 0.1%
 
1097271< 0.1%
 
605991< 0.1%
 
380721< 0.1%
 
401211< 0.1%
 
Other values (189563)189563> 99.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
91< 0.1%
 
101< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
2195471< 0.1%
 
2195461< 0.1%
 
2195451< 0.1%
 
2195441< 0.1%
 
2195431< 0.1%
 
2195411< 0.1%
 
2195391< 0.1%
 
2195381< 0.1%
 
2195371< 0.1%
 
2195361< 0.1%
 

INCKEY
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count189588
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138402.59221047745
Minimum1001
Maximum331342
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:06.282137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile28542.35
Q169096.75
median120591.5
Q3199153.25
95-th percentile312934.3
Maximum331342
Range330341
Interquartile range (IQR)130056.5

Descriptive statistics

Standard deviation85301.47229
Coefficient of variation (CV)0.616328574
Kurtosis-0.5747196898
Mean138402.5922
Median Absolute Deviation (MAD)59937.5
Skewness0.6321255947
Sum2.623947065e+10
Variance7276341175
2020-09-03T16:09:06.359515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2662381< 0.1%
 
1859681< 0.1%
 
897291< 0.1%
 
876801< 0.1%
 
1757431< 0.1%
 
1798371< 0.1%
 
1675471< 0.1%
 
1716411< 0.1%
 
1921191< 0.1%
 
1900701< 0.1%
 
2943371< 0.1%
 
1429591< 0.1%
 
1102391< 0.1%
 
3188341< 0.1%
 
1450041< 0.1%
 
1347631< 0.1%
 
1327141< 0.1%
 
1388571< 0.1%
 
1368081< 0.1%
 
1593351< 0.1%
 
1572861< 0.1%
 
1634291< 0.1%
 
856351< 0.1%
 
958761< 0.1%
 
979251< 0.1%
 
Other values (189563)189563> 99.9%
 
ValueCountFrequency (%) 
10011< 0.1%
 
10021< 0.1%
 
10031< 0.1%
 
10041< 0.1%
 
10051< 0.1%
 
10091< 0.1%
 
10111< 0.1%
 
10121< 0.1%
 
10131< 0.1%
 
10211< 0.1%
 
ValueCountFrequency (%) 
3313421< 0.1%
 
3313411< 0.1%
 
3313401< 0.1%
 
3313391< 0.1%
 
3313381< 0.1%
 
3313371< 0.1%
 
3313361< 0.1%
 
3313341< 0.1%
 
3313331< 0.1%
 
3313321< 0.1%
 

COLDETKEY
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count189588
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138596.7509283288
Minimum1001
Maximum332842
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:06.487843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile28542.35
Q169096.75
median120591.5
Q3199293.25
95-th percentile314374.3
Maximum332842
Range331841
Interquartile range (IQR)130196.5

Descriptive statistics

Standard deviation85638.25462
Coefficient of variation (CV)0.6178951097
Kurtosis-0.5609610871
Mean138596.7509
Median Absolute Deviation (MAD)59965.5
Skewness0.6399379141
Sum2.627628082e+10
Variance7333910654
2020-09-03T16:09:06.565446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2662381< 0.1%
 
1570301< 0.1%
 
1427031< 0.1%
 
1406541< 0.1%
 
1467971< 0.1%
 
1447481< 0.1%
 
1345071< 0.1%
 
3090531< 0.1%
 
1386011< 0.1%
 
1365521< 0.1%
 
1590791< 0.1%
 
1631731< 0.1%
 
1202081< 0.1%
 
1488341< 0.1%
 
1549771< 0.1%
 
1529281< 0.1%
 
2409911< 0.1%
 
2389421< 0.1%
 
2450851< 0.1%
 
2368891< 0.1%
 
2348401< 0.1%
 
2491711< 0.1%
 
1877611< 0.1%
 
1816181< 0.1%
 
1959571< 0.1%
 
Other values (189563)189563> 99.9%
 
ValueCountFrequency (%) 
10011< 0.1%
 
10021< 0.1%
 
10031< 0.1%
 
10041< 0.1%
 
10051< 0.1%
 
10091< 0.1%
 
10111< 0.1%
 
10121< 0.1%
 
10131< 0.1%
 
10211< 0.1%
 
ValueCountFrequency (%) 
3328421< 0.1%
 
3328411< 0.1%
 
3328401< 0.1%
 
3328391< 0.1%
 
3328381< 0.1%
 
3328371< 0.1%
 
3328361< 0.1%
 
3328341< 0.1%
 
3328331< 0.1%
 
3328321< 0.1%
 

REPORTNO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count189585
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1780512
 
2
1782439
 
2
1776526
 
2
2622350
 
1
EA05613
 
1
Other values (189580)
189580
ValueCountFrequency (%) 
17805122< 0.1%
 
17824392< 0.1%
 
17765262< 0.1%
 
26223501< 0.1%
 
EA056131< 0.1%
 
33761111< 0.1%
 
17906301< 0.1%
 
33258471< 0.1%
 
37527191< 0.1%
 
27982931< 0.1%
 
EA068161< 0.1%
 
E9005161< 0.1%
 
33260341< 0.1%
 
26038671< 0.1%
 
32108991< 0.1%
 
12135971< 0.1%
 
33357101< 0.1%
 
37806701< 0.1%
 
E4668101< 0.1%
 
26038721< 0.1%
 
17924551< 0.1%
 
17731581< 0.1%
 
28072291< 0.1%
 
27951541< 0.1%
 
29000691< 0.1%
 
Other values (189560)189560> 99.9%
 
2020-09-03T16:09:07.625658image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.998807941
Min length5

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
320016615.1%
 
215297511.5%
 
714562011.0%
 
61232449.3%
 
81211979.1%
 
51200469.0%
 
11153108.7%
 
01106378.3%
 
91075378.1%
 
4980657.4%
 
E243891.8%
 
C67730.5%
 
A9210.1%
 
_10< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number129479797.6%
 
Uppercase Letter320832.4%
 
Connector Punctuation10< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
320016615.5%
 
215297511.8%
 
714562011.2%
 
61232449.5%
 
81211979.4%
 
51200469.3%
 
11153108.9%
 
01106378.5%
 
91075378.3%
 
4980657.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E2438976.0%
 
C677321.1%
 
A9212.9%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common129480797.6%
 
Latin320832.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
320016615.5%
 
215297511.8%
 
714562011.2%
 
61232449.5%
 
81211979.4%
 
51200469.3%
 
11153108.9%
 
01106378.5%
 
91075378.3%
 
4980657.6%
 
_10< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E2438976.0%
 
C677321.1%
 
A9212.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1326890100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
320016615.1%
 
215297511.5%
 
714562011.0%
 
61232449.3%
 
81211979.1%
 
51200469.0%
 
11153108.7%
 
01106378.3%
 
91075378.1%
 
4980657.4%
 
E243891.8%
 
C67730.5%
 
A9210.1%
 
_10< 0.1%
 

STATUS
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Matched
189585
Unmatched
 
3
ValueCountFrequency (%) 
Matched189585> 99.9%
 
Unmatched3< 0.1%
 
2020-09-03T16:09:08.359322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.000031648
Min length7

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a18958814.3%
 
t18958814.3%
 
c18958814.3%
 
h18958814.3%
 
e18958814.3%
 
d18958814.3%
 
M18958514.3%
 
U3< 0.1%
 
n3< 0.1%
 
m3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter113753485.7%
 
Uppercase Letter18958814.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M189585> 99.9%
 
U3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a18958816.7%
 
t18958816.7%
 
c18958816.7%
 
h18958816.7%
 
e18958816.7%
 
d18958816.7%
 
n3< 0.1%
 
m3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1327122100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a18958814.3%
 
t18958814.3%
 
c18958814.3%
 
h18958814.3%
 
e18958814.3%
 
d18958814.3%
 
M18958514.3%
 
U3< 0.1%
 
n3< 0.1%
 
m3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1327122100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a18958814.3%
 
t18958814.3%
 
c18958814.3%
 
h18958814.3%
 
e18958814.3%
 
d18958814.3%
 
M18958514.3%
 
U3< 0.1%
 
n3< 0.1%
 
m3< 0.1%
 

ADDRTYPE
Categorical

Distinct count3
Unique (%)< 0.1%
Missing1818
Missing (%)1.0%
Memory size1.4 MiB
Block
123553
Intersection
63470
Alley
 
747
ValueCountFrequency (%) 
Block12355365.2%
 
Intersection6347033.5%
 
Alley7470.4%
 
(Missing)18181.0%
 
2020-09-03T16:09:09.089606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length12
Median length5
Mean length7.324271578
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
c18702313.5%
 
o18702313.5%
 
n1305769.4%
 
e1276879.2%
 
t1269409.1%
 
l1250479.0%
 
B1235538.9%
 
k1235538.9%
 
I634704.6%
 
r634704.6%
 
s634704.6%
 
i634704.6%
 
a18180.1%
 
A7470.1%
 
y7470.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter120082486.5%
 
Uppercase Letter18777013.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B12355365.8%
 
I6347033.8%
 
A7470.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
c18702315.6%
 
o18702315.6%
 
n13057610.9%
 
e12768710.6%
 
t12694010.6%
 
l12504710.4%
 
k12355310.3%
 
r634705.3%
 
s634705.3%
 
i634705.3%
 
a18180.2%
 
y7470.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1388594100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
c18702313.5%
 
o18702313.5%
 
n1305769.4%
 
e1276879.2%
 
t1269409.1%
 
l1250479.0%
 
B1235538.9%
 
k1235538.9%
 
I634704.6%
 
r634704.6%
 
s634704.6%
 
i634704.6%
 
a18180.1%
 
A7470.1%
 
y7470.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1388594100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
c18702313.5%
 
o18702313.5%
 
n1305769.4%
 
e1276879.2%
 
t1269409.1%
 
l1250479.0%
 
B1235538.9%
 
k1235538.9%
 
I634704.6%
 
r634704.6%
 
s634704.6%
 
i634704.6%
 
a18180.1%
 
A7470.1%
 
y7470.1%
 

INTKEY
Real number (ℝ≥0)

MISSING

Distinct count7555
Unique (%)11.9%
Missing126118
Missing (%)66.5%
Infinite0
Infinite (%)0.0%
Mean37502.2138175516
Minimum23807.0
Maximum757580.0
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:09.164198image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum23807
5-th percentile24509
Q128667
median29973
Q333984
95-th percentile37438
Maximum757580
Range733773
Interquartile range (IQR)5317

Descriptive statistics

Standard deviation51365.5819
Coefficient of variation (CV)1.369667992
Kurtosis72.33040007
Mean37502.21382
Median Absolute Deviation (MAD)2855
Skewness8.317251873
Sum2380265511
Variance2638423004
2020-09-03T16:09:09.234027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
299732420.1%
 
299331570.1%
 
295491350.1%
 
299131350.1%
 
335121260.1%
 
297611260.1%
 
299301240.1%
 
295761140.1%
 
298781140.1%
 
293801130.1%
 
290521120.1%
 
296221100.1%
 
299291020.1%
 
364191010.1%
 
299631010.1%
 
304101000.1%
 
29515960.1%
 
33135960.1%
 
29865950.1%
 
30482950.1%
 
29615950.1%
 
3050992< 0.1%
 
3414791< 0.1%
 
2876090< 0.1%
 
2991490< 0.1%
 
Other values (7530)6061832.0%
 
(Missing)12611866.5%
 
ValueCountFrequency (%) 
238075< 0.1%
 
238082< 0.1%
 
238111< 0.1%
 
238141< 0.1%
 
238152< 0.1%
 
238331< 0.1%
 
238433< 0.1%
 
238555< 0.1%
 
2386052< 0.1%
 
238612< 0.1%
 
ValueCountFrequency (%) 
7575801< 0.1%
 
7198621< 0.1%
 
7018171< 0.1%
 
6923451< 0.1%
 
6739742< 0.1%
 
6734741< 0.1%
 
6734711< 0.1%
 
6623162< 0.1%
 
6416261< 0.1%
 
6397712< 0.1%
 

LOCATION
Categorical

HIGH CARDINALITY
MISSING

Distinct count23951
Unique (%)12.8%
Missing2565
Missing (%)1.4%
Memory size1.4 MiB
BATTERY ST TUNNEL NB BETWEEN ALASKAN WY VI NB AND AURORA AVE N
 
274
BATTERY ST TUNNEL SB BETWEEN AURORA AVE N AND ALASKAN WY VI SB
 
268
N NORTHGATE WAY BETWEEN MERIDIAN AVE N AND CORLISS AVE N
 
260
AURORA AVE N BETWEEN N 117TH PL AND N 125TH ST
 
247
6TH AVE AND JAMES ST
 
242
Other values (23946)
185732
ValueCountFrequency (%) 
BATTERY ST TUNNEL NB BETWEEN ALASKAN WY VI NB AND AURORA AVE N2740.1%
 
BATTERY ST TUNNEL SB BETWEEN AURORA AVE N AND ALASKAN WY VI SB2680.1%
 
N NORTHGATE WAY BETWEEN MERIDIAN AVE N AND CORLISS AVE N2600.1%
 
AURORA AVE N BETWEEN N 117TH PL AND N 125TH ST2470.1%
 
6TH AVE AND JAMES ST2420.1%
 
ALASKAN WY VI NB BETWEEN S ROYAL BROUGHAM WAY ON RP AND SENECA ST OFF RP2370.1%
 
AURORA AVE N BETWEEN N 130TH ST AND N 135TH ST2350.1%
 
RAINIER AVE S BETWEEN S BAYVIEW ST AND S MCCLELLAN ST2290.1%
 
WEST SEATTLE BR EB BETWEEN ALASKAN WY VI NB ON RP AND DELRIDGE-W SEATTLE BR EB ON RP2090.1%
 
ALASKAN WY VI SB BETWEEN COLUMBIA ST ON RP AND ALASKAN WY VI SB EFR OFF RP2070.1%
 
AURORA BR BETWEEN RAYE ST AND BRIDGE WAY N1820.1%
 
1ST AVE BETWEEN BLANCHARD ST AND BELL ST1610.1%
 
ALASKAN WY VI NB BETWEEN SENECA ST OFF RP AND WESTERN AV OFF RP1610.1%
 
5TH AVE AND SPRING ST1570.1%
 
RAINIER AVE S BETWEEN S HENDERSON ST AND S DIRECTOR N ST1510.1%
 
RAINIER AVE S BETWEEN S DEARBORN ST AND S CHARLES N ST1440.1%
 
RAINIER AVE S BETWEEN S CHARLESTOWN ST AND S ANDOVER ST1400.1%
 
5TH AVE AND VIRGINIA ST1350.1%
 
5TH AVE AND UNION ST1350.1%
 
NE NORTHGATE WAY BETWEEN 5TH AVE NE AND 8TH AVE NE1340.1%
 
NE NORTHGATE WAY BETWEEN 3RD AVE NE AND 5TH AVE NE1300.1%
 
OLSON PL SW BETWEEN 1ST AVE S AND 2ND AVE SW1290.1%
 
RAINIER AVE S BETWEEN S CLOVERDALE ST AND S HENDERSON ST1280.1%
 
MONTLAKE BLVD NE BETWEEN NE PACIFIC PL AND 25TH AVE NE1260.1%
 
12TH AVE AND E MADISON ST1260.1%
 
Other values (23926)18247696.2%
 
(Missing)25651.4%
 
2020-09-03T16:09:09.983894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length90
Median length45
Mean length40.99567483
Min length3

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories (?)5
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
160638020.7%
 
E92759511.9%
 
N7062319.1%
 
A6649928.6%
 
T6102547.9%
 
S5086586.5%
 
W3107204.0%
 
D3014573.9%
 
R2456333.2%
 
V2367213.0%
 
H1980052.5%
 
B1830582.4%
 
O1663542.1%
 
L1546512.0%
 
I1363731.8%
 
Y930891.2%
 
1754771.0%
 
M614480.8%
 
5554850.7%
 
C533950.7%
 
P472860.6%
 
K470220.6%
 
G465570.6%
 
2444880.6%
 
4424170.5%
 
Other values (15)2485423.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter579197074.5%
 
Space Separator160638020.7%
 
Decimal Number3651024.7%
 
Lowercase Letter76950.1%
 
Dash Punctuation1141< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
17547720.7%
 
55548515.2%
 
24448812.2%
 
44241711.6%
 
33918410.7%
 
0257857.1%
 
6236186.5%
 
7221476.1%
 
8213015.8%
 
9152004.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E92759516.0%
 
N70623112.2%
 
A66499211.5%
 
T61025410.5%
 
S5086588.8%
 
W3107205.4%
 
D3014575.2%
 
R2456334.2%
 
V2367214.1%
 
H1980053.4%
 
B1830583.2%
 
O1663542.9%
 
L1546512.7%
 
I1363732.4%
 
Y930891.6%
 
M614481.1%
 
C533950.9%
 
P472860.8%
 
K470220.8%
 
G465570.8%
 
U407500.7%
 
F317200.5%
 
J144080.2%
 
X31410.1%
 
Q2010< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1606380100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n513066.7%
 
a256533.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1141100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin579966574.6%
 
Common197262325.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
160638081.4%
 
1754773.8%
 
5554852.8%
 
2444882.3%
 
4424172.2%
 
3391842.0%
 
0257851.3%
 
6236181.2%
 
7221471.1%
 
8213011.1%
 
9152000.8%
 
-11410.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E92759516.0%
 
N70623112.2%
 
A66499211.5%
 
T61025410.5%
 
S5086588.8%
 
W3107205.4%
 
D3014575.2%
 
R2456334.2%
 
V2367214.1%
 
H1980053.4%
 
B1830583.2%
 
O1663542.9%
 
L1546512.7%
 
I1363732.4%
 
Y930891.6%
 
M614481.1%
 
C533950.9%
 
P472860.8%
 
K470220.8%
 
G465570.8%
 
U407500.7%
 
F317200.5%
 
J144080.2%
 
n51300.1%
 
X31410.1%
 
Other values (3)50170.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7772288100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
160638020.7%
 
E92759511.9%
 
N7062319.1%
 
A6649928.6%
 
T6102547.9%
 
S5086586.5%
 
W3107204.0%
 
D3014573.9%
 
R2456333.2%
 
V2367213.0%
 
H1980052.5%
 
B1830582.4%
 
O1663542.1%
 
L1546512.0%
 
I1363731.8%
 
Y930891.2%
 
1754771.0%
 
M614480.8%
 
5554850.7%
 
C533950.7%
 
P472860.6%
 
K470220.6%
 
G465570.6%
 
2444880.6%
 
4424170.5%
 
Other values (15)2485423.2%
 

EXCEPTRSNCODE
Categorical

MISSING

Distinct count2
Unique (%)< 0.1%
Missing109777
Missing (%)57.9%
Memory size1.4 MiB
74948
NEI
 
4863
ValueCountFrequency (%) 
7494839.5%
 
NEI48632.6%
 
(Missing)10977757.9%
 
2020-09-03T16:09:10.666306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.209359242
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n21955452.4%
 
a10977726.2%
 
7494817.9%
 
N48631.2%
 
E48631.2%
 
I48631.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter32933178.6%
 
Space Separator7494817.9%
 
Uppercase Letter145893.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
74948100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n21955466.7%
 
a10977733.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N486333.3%
 
E486333.3%
 
I486333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin34392082.1%
 
Common7494817.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
74948100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n21955463.8%
 
a10977731.9%
 
N48631.4%
 
E48631.4%
 
I48631.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII418868100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n21955452.4%
 
a10977726.2%
 
7494817.9%
 
N48631.2%
 
E48631.2%
 
I48631.2%
 

EXCEPTRSNDESC
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing184725
Missing (%)97.4%
Memory size1.4 MiB
Not Enough Information, or Insufficient Location Information
4863
ValueCountFrequency (%) 
Not Enough Information, or Insufficient Location Information48632.6%
 
(Missing)18472597.4%
 
2020-09-03T16:09:11.393066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length60
Median length3
Mean length4.462070384
Min length3

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n40835448.3%
 
a19931423.6%
 
o437675.2%
 
291783.4%
 
t243152.9%
 
i243152.9%
 
f194522.3%
 
I145891.7%
 
r145891.7%
 
u97261.1%
 
m97261.1%
 
c97261.1%
 
N48630.6%
 
E48630.6%
 
g48630.6%
 
h48630.6%
 
,48630.6%
 
s48630.6%
 
e48630.6%
 
L48630.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter78273692.5%
 
Uppercase Letter291783.4%
 
Space Separator291783.4%
 
Other Punctuation48630.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n40835452.2%
 
a19931425.5%
 
o437675.6%
 
t243153.1%
 
i243153.1%
 
f194522.5%
 
r145891.9%
 
u97261.2%
 
m97261.2%
 
c97261.2%
 
g48630.6%
 
h48630.6%
 
s48630.6%
 
e48630.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I1458950.0%
 
N486316.7%
 
E486316.7%
 
L486316.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
29178100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,4863100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin81191496.0%
 
Common340414.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n40835450.3%
 
a19931424.5%
 
o437675.4%
 
t243153.0%
 
i243153.0%
 
f194522.4%
 
I145891.8%
 
r145891.8%
 
u97261.2%
 
m97261.2%
 
c97261.2%
 
N48630.6%
 
E48630.6%
 
g48630.6%
 
h48630.6%
 
s48630.6%
 
e48630.6%
 
L48630.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
2917885.7%
 
,486314.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII845955100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n40835448.3%
 
a19931423.6%
 
o437675.2%
 
291783.4%
 
t243152.9%
 
i243152.9%
 
f194522.3%
 
I145891.7%
 
r145891.7%
 
u97261.1%
 
m97261.1%
 
c97261.1%
 
N48630.6%
 
E48630.6%
 
g48630.6%
 
h48630.6%
 
,48630.6%
 
s48630.6%
 
e48630.6%
 
L48630.6%
 

SEVERITYCODE.1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
132627
2
56961
ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 
2020-09-03T16:09:12.115426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number189588100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common189588100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII189588100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113262770.0%
 
25696130.0%
 

SEVERITYDESC
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Property Damage Only Collision
132627
Injury Collision
56961
ValueCountFrequency (%) 
Property Damage Only Collision13262770.0%
 
Injury Collision5696130.0%
 
2020-09-03T16:09:12.839574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length30
Median length30
Mean length25.79375277
Min length16

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o51180310.5%
 
l51180310.5%
 
4548429.3%
 
n3791767.8%
 
i3791767.8%
 
r3222156.6%
 
y3222156.6%
 
e2652545.4%
 
a2652545.4%
 
C1895883.9%
 
s1895883.9%
 
P1326272.7%
 
p1326272.7%
 
t1326272.7%
 
D1326272.7%
 
m1326272.7%
 
g1326272.7%
 
O1326272.7%
 
I569611.2%
 
j569611.2%
 
u569611.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter379091477.5%
 
Uppercase Letter64443013.2%
 
Space Separator4548429.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C18958829.4%
 
P13262720.6%
 
D13262720.6%
 
O13262720.6%
 
I569618.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o51180313.5%
 
l51180313.5%
 
n37917610.0%
 
i37917610.0%
 
r3222158.5%
 
y3222158.5%
 
e2652547.0%
 
a2652547.0%
 
s1895885.0%
 
p1326273.5%
 
t1326273.5%
 
m1326273.5%
 
g1326273.5%
 
j569611.5%
 
u569611.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
454842100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin443534490.7%
 
Common4548429.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o51180311.5%
 
l51180311.5%
 
n3791768.5%
 
i3791768.5%
 
r3222157.3%
 
y3222157.3%
 
e2652546.0%
 
a2652546.0%
 
C1895884.3%
 
s1895884.3%
 
P1326273.0%
 
p1326273.0%
 
t1326273.0%
 
D1326273.0%
 
m1326273.0%
 
g1326273.0%
 
O1326273.0%
 
I569611.3%
 
j569611.3%
 
u569611.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
454842100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4890186100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o51180310.5%
 
l51180310.5%
 
4548429.3%
 
n3791767.8%
 
i3791767.8%
 
r3222156.6%
 
y3222156.6%
 
e2652545.4%
 
a2652545.4%
 
C1895883.9%
 
s1895883.9%
 
P1326272.7%
 
p1326272.7%
 
t1326272.7%
 
D1326272.7%
 
m1326272.7%
 
g1326272.7%
 
O1326272.7%
 
I569611.2%
 
j569611.2%
 
u569611.2%
 

COLLISIONTYPE
Categorical

HIGH CORRELATION

Distinct count10
Unique (%)< 0.1%
Missing20
Missing (%)< 0.1%
Memory size1.4 MiB
Parked Car
47987
Angles
34674
Rear Ended
34090
Other
23702
Sideswipe
18609
Other values (5)
30506
ValueCountFrequency (%) 
Parked Car4798725.3%
 
Angles3467418.3%
 
Rear Ended3409018.0%
 
Other2370212.5%
 
Sideswipe186099.8%
 
Left Turn137037.2%
 
Pedestrian66073.5%
 
Cycles52162.8%
 
Right Turn29561.6%
 
Head On20241.1%
 
(Missing)20< 0.1%
 
2020-09-03T16:09:13.563550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.330094732
Min length3

Overview of Unicode Properties

Unique unicode characters29
Unique unicode categories (?)3
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e24591815.6%
 
r17703211.2%
 
d1434079.1%
 
a1387158.8%
 
1007606.4%
 
n940946.0%
 
s651064.1%
 
P545943.5%
 
C532033.4%
 
k479873.0%
 
t469683.0%
 
i467813.0%
 
l398902.5%
 
g376302.4%
 
R370462.3%
 
A346742.2%
 
E340902.2%
 
h266581.7%
 
O257261.6%
 
S186091.2%
 
w186091.2%
 
p186091.2%
 
T166591.1%
 
u166591.1%
 
L137030.9%
 
Other values (4)261591.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter118819875.2%
 
Uppercase Letter29032818.4%
 
Space Separator1007606.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P5459418.8%
 
C5320318.3%
 
R3704612.8%
 
A3467411.9%
 
E3409011.7%
 
O257268.9%
 
S186096.4%
 
T166595.7%
 
L137034.7%
 
H20240.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e24591820.7%
 
r17703214.9%
 
d14340712.1%
 
a13871511.7%
 
n940947.9%
 
s651065.5%
 
k479874.0%
 
t469684.0%
 
i467813.9%
 
l398903.4%
 
g376303.2%
 
h266582.2%
 
w186091.6%
 
p186091.6%
 
u166591.4%
 
f137031.2%
 
y52160.4%
 
c52160.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
100760100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin147852693.6%
 
Common1007606.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e24591816.6%
 
r17703212.0%
 
d1434079.7%
 
a1387159.4%
 
n940946.4%
 
s651064.4%
 
P545943.7%
 
C532033.6%
 
k479873.2%
 
t469683.2%
 
i467813.2%
 
l398902.7%
 
g376302.5%
 
R370462.5%
 
A346742.3%
 
E340902.3%
 
h266581.8%
 
O257261.7%
 
S186091.3%
 
w186091.3%
 
p186091.3%
 
T166591.1%
 
u166591.1%
 
L137030.9%
 
f137030.9%
 
Other values (3)124560.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
100760100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1579286100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e24591815.6%
 
r17703211.2%
 
d1434079.1%
 
a1387158.8%
 
1007606.4%
 
n940946.0%
 
s651064.1%
 
P545943.5%
 
C532033.4%
 
k479873.0%
 
t469683.0%
 
i467813.0%
 
l398902.5%
 
g376302.4%
 
R370462.3%
 
A346742.2%
 
E340902.2%
 
h266581.7%
 
O257261.6%
 
S186091.2%
 
w186091.2%
 
p186091.2%
 
T166591.1%
 
u166591.1%
 
L137030.9%
 
Other values (4)261591.7%
 

PERSONCOUNT
Real number (ℝ≥0)

ZEROS

Distinct count47
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.460060763339452
Minimum0
Maximum81
Zeros5536
Zeros (%)2.9%
Memory size1.4 MiB
2020-09-03T16:09:13.642854image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum81
Range81
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.355109203
Coefficient of variation (CV)0.5508437933
Kurtosis201.4377056
Mean2.460060763
Median Absolute Deviation (MAD)0
Skewness7.280497106
Sum466398
Variance1.836320951
2020-09-03T16:09:13.724215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
211131158.7%
 
33514118.5%
 
4144457.6%
 
1116246.1%
 
565843.5%
 
055362.9%
 
627021.4%
 
711310.6%
 
85330.3%
 
92160.1%
 
101280.1%
 
1156< 0.1%
 
1233< 0.1%
 
1321< 0.1%
 
1419< 0.1%
 
1511< 0.1%
 
1711< 0.1%
 
168< 0.1%
 
446< 0.1%
 
186< 0.1%
 
206< 0.1%
 
256< 0.1%
 
195< 0.1%
 
264< 0.1%
 
224< 0.1%
 
Other values (22)41< 0.1%
 
ValueCountFrequency (%) 
055362.9%
 
1116246.1%
 
211131158.7%
 
33514118.5%
 
4144457.6%
 
565843.5%
 
627021.4%
 
711310.6%
 
85330.3%
 
92160.1%
 
ValueCountFrequency (%) 
811< 0.1%
 
571< 0.1%
 
541< 0.1%
 
531< 0.1%
 
481< 0.1%
 
473< 0.1%
 
446< 0.1%
 
431< 0.1%
 
411< 0.1%
 
391< 0.1%
 

PEDCOUNT
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03770280819461147
Minimum0
Maximum6
Zeros182730
Zeros (%)96.4%
Memory size1.4 MiB
2020-09-03T16:09:13.808452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1996703324
Coefficient of variation (CV)5.295900808
Kurtosis41.96801158
Mean0.03770280819
Median Absolute Deviation (MAD)0
Skewness5.785153252
Sum7148
Variance0.03986824164
2020-09-03T16:09:13.882255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018273096.4%
 
166053.5%
 
22250.1%
 
322< 0.1%
 
44< 0.1%
 
61< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
018273096.4%
 
166053.5%
 
22250.1%
 
322< 0.1%
 
44< 0.1%
 
51< 0.1%
 
61< 0.1%
 
ValueCountFrequency (%) 
61< 0.1%
 
51< 0.1%
 
44< 0.1%
 
322< 0.1%
 
22250.1%
 
166053.5%
 
018273096.4%
 

PEDCYLCOUNT
Categorical

Distinct count3
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
184305
1
 
5254
2
 
29
ValueCountFrequency (%) 
018430597.2%
 
152542.8%
 
229< 0.1%
 
2020-09-03T16:09:14.601736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
018430597.2%
 
152542.8%
 
229< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number189588100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
018430597.2%
 
152542.8%
 
229< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common189588100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
018430597.2%
 
152542.8%
 
229< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII189588100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
018430597.2%
 
152542.8%
 
229< 0.1%
 

VEHCOUNT
Real number (ℝ≥0)

Distinct count12
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9722978247568412
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:14.678995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5543389598
Coefficient of variation (CV)0.2810625012
Kurtosis13.24575653
Mean1.972297825
Median Absolute Deviation (MAD)0
Skewness1.660267017
Sum373924
Variance0.3072916823
2020-09-03T16:09:14.749388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
214765077.9%
 
12574813.6%
 
3130106.9%
 
424261.3%
 
55290.3%
 
61460.1%
 
746< 0.1%
 
815< 0.1%
 
99< 0.1%
 
116< 0.1%
 
102< 0.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
12574813.6%
 
214765077.9%
 
3130106.9%
 
424261.3%
 
55290.3%
 
61460.1%
 
746< 0.1%
 
815< 0.1%
 
99< 0.1%
 
102< 0.1%
 
ValueCountFrequency (%) 
121< 0.1%
 
116< 0.1%
 
102< 0.1%
 
99< 0.1%
 
815< 0.1%
 
746< 0.1%
 
61460.1%
 
55290.3%
 
424261.3%
 
3130106.9%
 

INCDATE
Categorical

HIGH CARDINALITY

Distinct count5948
Unique (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2006/11/02 00:00:00+00
 
96
2008/10/03 00:00:00+00
 
92
2005/05/18 00:00:00+00
 
84
2005/11/05 00:00:00+00
 
83
2006/01/13 00:00:00+00
 
83
Other values (5943)
189150
ValueCountFrequency (%) 
2006/11/02 00:00:00+00960.1%
 
2008/10/03 00:00:00+0092< 0.1%
 
2005/05/18 00:00:00+0084< 0.1%
 
2005/11/05 00:00:00+0083< 0.1%
 
2006/01/13 00:00:00+0083< 0.1%
 
2008/10/31 00:00:00+0082< 0.1%
 
2005/04/29 00:00:00+0076< 0.1%
 
2005/04/15 00:00:00+0075< 0.1%
 
2007/10/19 00:00:00+0074< 0.1%
 
2004/12/04 00:00:00+0074< 0.1%
 
2007/07/20 00:00:00+0073< 0.1%
 
2006/06/01 00:00:00+0073< 0.1%
 
2005/10/28 00:00:00+0073< 0.1%
 
2006/11/04 00:00:00+0070< 0.1%
 
2010/11/22 00:00:00+0070< 0.1%
 
2007/11/15 00:00:00+0070< 0.1%
 
2006/10/18 00:00:00+0070< 0.1%
 
2006/11/22 00:00:00+0069< 0.1%
 
2005/11/04 00:00:00+0069< 0.1%
 
2016/10/13 00:00:00+0069< 0.1%
 
2006/11/10 00:00:00+0068< 0.1%
 
2006/11/21 00:00:00+0068< 0.1%
 
2005/11/11 00:00:00+0068< 0.1%
 
2010/10/09 00:00:00+0068< 0.1%
 
2007/01/05 00:00:00+0068< 0.1%
 
Other values (5923)18772399.0%
 
2020-09-03T16:09:15.466329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories (?)4
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0203335748.8%
 
/3791769.1%
 
:3791769.1%
 
23094687.4%
 
12834966.8%
 
1895884.5%
 
+1895884.5%
 
5628881.5%
 
6608201.5%
 
7594991.4%
 
8583521.4%
 
4574421.4%
 
9542671.3%
 
3538191.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number303340872.7%
 
Other Punctuation75835218.2%
 
Space Separator1895884.5%
 
Math Symbol1895884.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0203335767.0%
 
230946810.2%
 
12834969.3%
 
5628882.1%
 
6608202.0%
 
7594992.0%
 
8583521.9%
 
4574421.9%
 
9542671.8%
 
3538191.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/37917650.0%
 
:37917650.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
189588100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+189588100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4170936100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0203335748.8%
 
/3791769.1%
 
:3791769.1%
 
23094687.4%
 
12834966.8%
 
1895884.5%
 
+1895884.5%
 
5628881.5%
 
6608201.5%
 
7594991.4%
 
8583521.4%
 
4574421.4%
 
9542671.3%
 
3538191.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4170936100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0203335748.8%
 
/3791769.1%
 
:3791769.1%
 
23094687.4%
 
12834966.8%
 
1895884.5%
 
+1895884.5%
 
5628881.5%
 
6608201.5%
 
7594991.4%
 
8583521.4%
 
4574421.4%
 
9542671.3%
 
3538191.3%
 

INCDTTM
Categorical

HIGH CARDINALITY

Distinct count160155
Unique (%)84.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
11/2/2006
 
96
10/3/2008
 
91
11/5/2005
 
83
12/4/2004
 
74
6/1/2006
 
73
Other values (160150)
189171
ValueCountFrequency (%) 
11/2/2006960.1%
 
10/3/200891< 0.1%
 
11/5/200583< 0.1%
 
12/4/200474< 0.1%
 
6/1/200673< 0.1%
 
11/4/200670< 0.1%
 
11/4/200569< 0.1%
 
11/6/200668< 0.1%
 
1/5/200768< 0.1%
 
5/5/200668< 0.1%
 
4/8/200668< 0.1%
 
11/1/200867< 0.1%
 
11/1/200567< 0.1%
 
3/8/200665< 0.1%
 
10/6/200665< 0.1%
 
1/2/200464< 0.1%
 
1/9/200664< 0.1%
 
11/3/200664< 0.1%
 
8/6/200462< 0.1%
 
10/6/200562< 0.1%
 
6/9/200561< 0.1%
 
7/8/200561< 0.1%
 
5/6/200960< 0.1%
 
11/6/200860< 0.1%
 
4/3/200660< 0.1%
 
Other values (160130)18787899.1%
 
2020-09-03T16:09:16.469908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length22
Median length20
Mean length18.68217925
Min length8

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
074230421.0%
 
138678310.9%
 
/37917610.7%
 
237310510.5%
 
3279149.3%
 
:3279149.3%
 
M1639574.6%
 
51252383.5%
 
41084383.1%
 
31078503.0%
 
P1065763.0%
 
8854542.4%
 
6851752.4%
 
7851202.4%
 
9795322.2%
 
A573811.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number217899961.5%
 
Other Punctuation70709020.0%
 
Space Separator3279149.3%
 
Uppercase Letter3279149.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
074230434.1%
 
138678317.8%
 
237310517.1%
 
51252385.7%
 
41084385.0%
 
31078504.9%
 
8854543.9%
 
6851753.9%
 
7851203.9%
 
9795323.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/37917653.6%
 
:32791446.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
327914100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M16395750.0%
 
P10657632.5%
 
A5738117.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common321400390.7%
 
Latin3279149.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
074230423.1%
 
138678312.0%
 
/37917611.8%
 
237310511.6%
 
32791410.2%
 
:32791410.2%
 
51252383.9%
 
41084383.4%
 
31078503.4%
 
8854542.7%
 
6851752.7%
 
7851202.6%
 
9795322.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M16395750.0%
 
P10657632.5%
 
A5738117.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3541917100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
074230421.0%
 
138678310.9%
 
/37917610.7%
 
237310510.5%
 
3279149.3%
 
:3279149.3%
 
M1639574.6%
 
51252383.5%
 
41084383.1%
 
31078503.0%
 
P1065763.0%
 
8854542.4%
 
6851752.4%
 
7851202.4%
 
9795322.2%
 
A573811.6%
 

JUNCTIONTYPE
Categorical

MISSING

Distinct count7
Unique (%)< 0.1%
Missing6176
Missing (%)3.3%
Memory size1.4 MiB
Mid-Block (not related to intersection)
87034
At Intersection (intersection related)
61247
Mid-Block (but intersection related)
22376
Driveway Junction
 
10527
At Intersection (but not related to intersection)
 
2059
Other values (2)
 
169
ValueCountFrequency (%) 
Mid-Block (not related to intersection)8703445.9%
 
At Intersection (intersection related)6124732.3%
 
Mid-Block (but intersection related)2237611.8%
 
Driveway Junction105275.6%
 
At Intersection (but not related to intersection)20591.1%
 
Ramp Junction1620.1%
 
Unknown7< 0.1%
 
(Missing)61763.3%
 
2020-09-03T16:09:17.189000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length49
Median length38
Mean length36.01378252
Min length3

Overview of Unicode Properties

Unique unicode characters31
Unique unicode categories (?)6
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t92137613.5%
 
e82800312.1%
 
6220489.1%
 
n5948888.7%
 
i5393647.9%
 
o5343147.8%
 
r4192656.1%
 
c3561215.2%
 
l2821264.1%
 
d2821264.1%
 
s2360223.5%
 
a1895812.8%
 
(1727162.5%
 
)1727162.5%
 
k1094171.6%
 
M1094101.6%
 
-1094101.6%
 
B1094101.6%
 
A633060.9%
 
I633060.9%
 
u351240.5%
 
b244350.4%
 
J106890.2%
 
w105340.2%
 
D105270.2%
 
Other values (6)215470.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter538407478.9%
 
Space Separator6220489.1%
 
Uppercase Letter3668175.4%
 
Open Punctuation1727162.5%
 
Close Punctuation1727162.5%
 
Dash Punctuation1094101.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M10941029.8%
 
B10941029.8%
 
A6330617.3%
 
I6330617.3%
 
J106892.9%
 
D105272.9%
 
R162< 0.1%
 
U7< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t92137617.1%
 
e82800315.4%
 
n59488811.0%
 
i53936410.0%
 
o5343149.9%
 
r4192657.8%
 
c3561216.6%
 
l2821265.2%
 
d2821265.2%
 
s2360224.4%
 
a1895813.5%
 
k1094172.0%
 
u351240.7%
 
b244350.5%
 
w105340.2%
 
v105270.2%
 
y105270.2%
 
m162< 0.1%
 
p162< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
622048100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(172716100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)172716100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-109410100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin575089184.2%
 
Common107689015.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t92137616.0%
 
e82800314.4%
 
n59488810.3%
 
i5393649.4%
 
o5343149.3%
 
r4192657.3%
 
c3561216.2%
 
l2821264.9%
 
d2821264.9%
 
s2360224.1%
 
a1895813.3%
 
k1094171.9%
 
M1094101.9%
 
B1094101.9%
 
A633061.1%
 
I633061.1%
 
u351240.6%
 
b244350.4%
 
J106890.2%
 
w105340.2%
 
D105270.2%
 
v105270.2%
 
y105270.2%
 
R162< 0.1%
 
m162< 0.1%
 
Other values (2)169< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
62204857.8%
 
(17271616.0%
 
)17271616.0%
 
-10941010.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6827781100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t92137613.5%
 
e82800312.1%
 
6220489.1%
 
n5948888.7%
 
i5393647.9%
 
o5343147.8%
 
r4192656.1%
 
c3561215.2%
 
l2821264.1%
 
d2821264.1%
 
s2360223.5%
 
a1895812.8%
 
(1727162.5%
 
)1727162.5%
 
k1094171.6%
 
M1094101.6%
 
-1094101.6%
 
B1094101.6%
 
A633060.9%
 
I633060.9%
 
u351240.5%
 
b244350.4%
 
J106890.2%
 
w105340.2%
 
D105270.2%
 
Other values (6)215470.3%
 

SDOT_COLCODE
Real number (ℝ≥0)

ZEROS

Distinct count38
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.884196257147076
Minimum0
Maximum69
Zeros8972
Zeros (%)4.7%
Memory size1.4 MiB
2020-09-03T16:09:17.268137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q111
median13
Q314
95-th percentile28
Maximum69
Range69
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.701585387
Coefficient of variation (CV)0.4826772298
Kurtosis9.748315952
Mean13.88419626
Median Absolute Deviation (MAD)2
Skewness2.096034059
Sum2632277
Variance44.9112467
2020-09-03T16:09:17.344295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
118329843.9%
 
145275527.8%
 
1698015.2%
 
089724.7%
 
2887724.6%
 
2463823.4%
 
1357983.1%
 
2646532.5%
 
1830201.6%
 
1515750.8%
 
1214290.8%
 
5112830.7%
 
294690.2%
 
211810.1%
 
561780.1%
 
271620.1%
 
541340.1%
 
231230.1%
 
481060.1%
 
311030.1%
 
251010.1%
 
3492< 0.1%
 
3353< 0.1%
 
5550< 0.1%
 
2216< 0.1%
 
Other values (13)82< 0.1%
 
ValueCountFrequency (%) 
089724.7%
 
118329843.9%
 
1214290.8%
 
1357983.1%
 
145275527.8%
 
1515750.8%
 
1698015.2%
 
1830201.6%
 
211810.1%
 
2216< 0.1%
 
ValueCountFrequency (%) 
6916< 0.1%
 
682< 0.1%
 
667< 0.1%
 
649< 0.1%
 
613< 0.1%
 
561780.1%
 
5550< 0.1%
 
541340.1%
 
539< 0.1%
 
522< 0.1%
 

SDOT_COLDESC
Categorical

Distinct count38
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLE
83298
MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR END
52755
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPE
 
9801
NOT ENOUGH INFORMATION / NOT APPLICABLE
 
8972
MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECT
 
8772
Other values (33)
25990
ValueCountFrequency (%) 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLE8329843.9%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR END5275527.8%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPE98015.2%
 
NOT ENOUGH INFORMATION / NOT APPLICABLE89724.7%
 
MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECT87724.6%
 
MOTOR VEHCILE STRUCK PEDESTRIAN63823.4%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE AT ANGLE57983.1%
 
MOTOR VEHICLE STRUCK OBJECT IN ROAD46532.5%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, FRONT END AT ANGLE30201.6%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, RIGHT SIDE SIDESWIPE15750.8%
 
MOTOR VEHICLE STRUCK MOTOR VEHICLE, RIGHT SIDE AT ANGLE14290.8%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLE12830.7%
 
MOTOR VEHICLE OVERTURNED IN ROAD4690.2%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, REAR END1810.1%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE LEFT SIDE SIDESWIPE1780.1%
 
MOTOR VEHICLE RAN OFF ROAD - NO COLLISION1620.1%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE REAR END1340.1%
 
MOTOR VEHICLE STRUCK PEDALCYCLIST, LEFT SIDE SIDESWIPE1230.1%
 
DRIVERLESS VEHICLE RAN OFF ROAD - HIT FIXED OBJECT1060.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE FRONT END AT ANGLE1030.1%
 
MOTOR VEHICLE STRUCK TRAIN1010.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE REAR END92< 0.1%
 
DRIVERLESS VEHICLE STRUCK MOTOR VEHICLE LEFT SIDE AT ANGLE53< 0.1%
 
PEDALCYCLIST STRUCK MOTOR VEHICLE RIGHT SIDE SIDESWIPE50< 0.1%
 
PEDALCYCLIST OVERTURNED IN ROAD16< 0.1%
 
Other values (13)82< 0.1%
 
2020-09-03T16:09:18.087782image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length60
Median length54
Mean length48.79027154
Min length26

Overview of Unicode Properties

Unique unicode characters28
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
131808914.2%
 
E107852611.7%
 
O8405499.1%
 
T7693328.3%
 
R7437818.0%
 
C5393435.8%
 
L4752085.1%
 
I4432314.8%
 
N3899424.2%
 
H3564473.9%
 
M3440893.7%
 
V3363783.6%
 
A3048683.3%
 
S2260242.4%
 
D2070572.2%
 
U1805452.0%
 
K1710881.8%
 
,1579961.7%
 
F1396081.5%
 
G1070691.2%
 
P411320.4%
 
B225150.2%
 
J135430.1%
 
W117560.1%
 
-90430.1%
 
Other values (3)228910.2%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter775595083.8%
 
Space Separator131808914.2%
 
Other Punctuation1669681.8%
 
Dash Punctuation90430.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E107852613.9%
 
O84054910.8%
 
T7693329.9%
 
R7437819.6%
 
C5393437.0%
 
L4752086.1%
 
I4432315.7%
 
N3899425.0%
 
H3564474.6%
 
M3440894.4%
 
V3363784.3%
 
A3048683.9%
 
S2260242.9%
 
D2070572.7%
 
U1805452.3%
 
K1710882.2%
 
F1396081.8%
 
G1070691.4%
 
P411320.5%
 
B225150.3%
 
J135430.2%
 
W117560.2%
 
X88800.1%
 
Y50390.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1318089100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,15799694.6%
 
/89725.4%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9043100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin775595083.8%
 
Common149410016.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E107852613.9%
 
O84054910.8%
 
T7693329.9%
 
R7437819.6%
 
C5393437.0%
 
L4752086.1%
 
I4432315.7%
 
N3899425.0%
 
H3564474.6%
 
M3440894.4%
 
V3363784.3%
 
A3048683.9%
 
S2260242.9%
 
D2070572.7%
 
U1805452.3%
 
K1710882.2%
 
F1396081.8%
 
G1070691.4%
 
P411320.5%
 
B225150.3%
 
J135430.2%
 
W117560.2%
 
X88800.1%
 
Y50390.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
131808988.2%
 
,15799610.6%
 
-90430.6%
 
/89720.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII9250050100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
131808914.2%
 
E107852611.7%
 
O8405499.1%
 
T7693328.3%
 
R7437818.0%
 
C5393435.8%
 
L4752085.1%
 
I4432314.8%
 
N3899424.2%
 
H3564473.9%
 
M3440893.7%
 
V3363783.6%
 
A3048683.3%
 
S2260242.4%
 
D2070572.2%
 
U1805452.0%
 
K1710881.8%
 
,1579961.7%
 
F1396081.5%
 
G1070691.2%
 
P411320.4%
 
B225150.2%
 
J135430.1%
 
W117560.1%
 
-90430.1%
 
Other values (3)228910.2%
 

INATTENTIONIND
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing159800
Missing (%)84.3%
Memory size1.4 MiB
Y
29788
ValueCountFrequency (%) 
Y2978815.7%
 
(Missing)15980084.3%
 
2020-09-03T16:09:18.814289image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.685760702
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n31960062.8%
 
a15980031.4%
 
Y297885.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter47940094.1%
 
Uppercase Letter297885.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n31960066.7%
 
a15980033.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y29788100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin509188100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n31960062.8%
 
a15980031.4%
 
Y297885.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII509188100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n31960062.8%
 
a15980031.4%
 
Y297885.9%
 

UNDERINFL
Categorical

Distinct count4
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
N
100143
0
80330
Y
 
5121
1
 
3994
ValueCountFrequency (%) 
N10014352.8%
 
08033042.4%
 
Y51212.7%
 
139942.1%
 
2020-09-03T16:09:19.541286image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)2
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10014352.8%
 
08033042.4%
 
Y51212.7%
 
139942.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10526455.5%
 
Decimal Number8432444.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10014395.1%
 
Y51214.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
08033095.3%
 
139944.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin10526455.5%
 
Common8432444.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10014395.1%
 
Y51214.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
08033095.3%
 
139944.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII189588100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10014352.8%
 
08033042.4%
 
Y51212.7%
 
139942.1%
 

WEATHER
Categorical

Distinct count11
Unique (%)< 0.1%
Missing194
Missing (%)0.1%
Memory size1.4 MiB
Clear
110987
Raining
33127
Overcast
27686
Unknown
 
15088
Snowing
 
907
Other values (6)
 
1599
ValueCountFrequency (%) 
Clear11098758.5%
 
Raining3312717.5%
 
Overcast2768614.6%
 
Unknown150888.0%
 
Snowing9070.5%
 
Other8310.4%
 
Fog/Smog/Smoke5690.3%
 
Sleet/Hail/Freezing Rain1130.1%
 
Blowing Sand/Dirt56< 0.1%
 
Severe Crosswind25< 0.1%
 
Partly Cloudy5< 0.1%
 
(Missing)1940.1%
 
2020-09-03T16:09:20.250688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length24
Median length5
Mean length5.997789945
Min length3

Overview of Unicode Properties

Unique unicode characters32
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a17228115.2%
 
e14060012.4%
 
r13972812.3%
 
n11408310.0%
 
l1112799.8%
 
C1110179.8%
 
i676375.9%
 
g353413.1%
 
R332402.9%
 
t286912.5%
 
O285172.5%
 
s277362.4%
 
v277112.4%
 
c276862.4%
 
o177881.6%
 
w160761.4%
 
k156571.4%
 
U150881.3%
 
S22390.2%
 
/14200.1%
 
m11380.1%
 
h8310.1%
 
F6820.1%
 
199< 0.1%
 
H113< 0.1%
 
Other values (7)331< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter94447783.1%
 
Uppercase Letter19101316.8%
 
Other Punctuation14200.1%
 
Space Separator199< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C11101758.1%
 
R3324017.4%
 
O2851714.9%
 
U150887.9%
 
S22391.2%
 
F6820.4%
 
H1130.1%
 
B56< 0.1%
 
D56< 0.1%
 
P5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a17228118.2%
 
e14060014.9%
 
r13972814.8%
 
n11408312.1%
 
l11127911.8%
 
i676377.2%
 
g353413.7%
 
t286913.0%
 
s277362.9%
 
v277112.9%
 
c276862.9%
 
o177881.9%
 
w160761.7%
 
k156571.7%
 
m11380.1%
 
h8310.1%
 
z113< 0.1%
 
d86< 0.1%
 
y10< 0.1%
 
u5< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1420100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
199100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin113549099.9%
 
Common16190.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a17228115.2%
 
e14060012.4%
 
r13972812.3%
 
n11408310.0%
 
l1112799.8%
 
C1110179.8%
 
i676376.0%
 
g353413.1%
 
R332402.9%
 
t286912.5%
 
O285172.5%
 
s277362.4%
 
v277112.4%
 
c276862.4%
 
o177881.6%
 
w160761.4%
 
k156571.4%
 
U150881.3%
 
S22390.2%
 
m11380.1%
 
h8310.1%
 
F6820.1%
 
H113< 0.1%
 
z113< 0.1%
 
d86< 0.1%
 
Other values (5)132< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/142087.7%
 
19912.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1137109100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a17228115.2%
 
e14060012.4%
 
r13972812.3%
 
n11408310.0%
 
l1112799.8%
 
C1110179.8%
 
i676375.9%
 
g353413.1%
 
R332402.9%
 
t286912.5%
 
O285172.5%
 
s277362.4%
 
v277112.4%
 
c276862.4%
 
o177881.6%
 
w160761.4%
 
k156571.4%
 
U150881.3%
 
S22390.2%
 
/14200.1%
 
m11380.1%
 
h8310.1%
 
F6820.1%
 
199< 0.1%
 
H113< 0.1%
 
Other values (7)331< 0.1%
 

ROADCOND
Categorical

Distinct count9
Unique (%)< 0.1%
Missing126
Missing (%)0.1%
Memory size1.4 MiB
Dry
124349
Wet
47446
Unknown
 
15075
Ice
 
1203
Snow/Slush
 
1004
Other values (4)
 
385
ValueCountFrequency (%) 
Dry12434965.6%
 
Wet4744625.0%
 
Unknown150758.0%
 
Ice12030.6%
 
Snow/Slush10040.5%
 
Other1310.1%
 
Standing Water1150.1%
 
Sand/Mud/Dirt75< 0.1%
 
Oil64< 0.1%
 
(Missing)1260.1%
 
2020-09-03T16:09:20.965042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.367138215
Min length3

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r12467019.5%
 
D12442419.5%
 
y12434919.5%
 
e488957.7%
 
t478827.5%
 
W475617.5%
 
n467867.3%
 
o160792.5%
 
w160792.5%
 
U150752.4%
 
k150752.4%
 
S21980.3%
 
I12030.2%
 
c12030.2%
 
/11540.2%
 
h11350.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
a4310.1%
 
d265< 0.1%
 
i254< 0.1%
 
O195< 0.1%
 
g115< 0.1%
 
115< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter44636969.9%
 
Uppercase Letter19073129.9%
 
Other Punctuation11540.2%
 
Space Separator115< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D12442465.2%
 
W4756124.9%
 
U150757.9%
 
S21981.2%
 
I12030.6%
 
O1950.1%
 
M75< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r12467027.9%
 
y12434927.9%
 
e4889511.0%
 
t4788210.7%
 
n4678610.5%
 
o160793.6%
 
w160793.6%
 
k150753.4%
 
c12030.3%
 
h11350.3%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
a4310.1%
 
d2650.1%
 
i2540.1%
 
g115< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1154100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
115100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin63710099.8%
 
Common12690.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r12467019.6%
 
D12442419.5%
 
y12434919.5%
 
e488957.7%
 
t478827.5%
 
W475617.5%
 
n467867.3%
 
o160792.5%
 
w160792.5%
 
U150752.4%
 
k150752.4%
 
S21980.3%
 
I12030.2%
 
c12030.2%
 
h11350.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
a4310.1%
 
d265< 0.1%
 
i254< 0.1%
 
O195< 0.1%
 
g115< 0.1%
 
M75< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
/115490.9%
 
1159.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII638369100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r12467019.5%
 
D12442419.5%
 
y12434919.5%
 
e488957.7%
 
t478827.5%
 
W475617.5%
 
n467867.3%
 
o160792.5%
 
w160792.5%
 
U150752.4%
 
k150752.4%
 
S21980.3%
 
I12030.2%
 
c12030.2%
 
/11540.2%
 
h11350.2%
 
u10790.2%
 
l10680.2%
 
s10040.2%
 
a4310.1%
 
d265< 0.1%
 
i254< 0.1%
 
O195< 0.1%
 
g115< 0.1%
 
115< 0.1%
 

LIGHTCOND
Categorical

Distinct count9
Unique (%)< 0.1%
Missing284
Missing (%)0.1%
Memory size1.4 MiB
Daylight
115977
Dark - Street Lights On
48479
Unknown
 
13472
Dusk
 
5897
Dawn
 
2498
Other values (4)
 
2981
ValueCountFrequency (%) 
Daylight11597761.2%
 
Dark - Street Lights On4847925.6%
 
Unknown134727.1%
 
Dusk58973.1%
 
Dawn24981.3%
 
Dark - No Street Lights15360.8%
 
Dark - Street Lights Off11990.6%
 
Other2350.1%
 
Dark - Unknown Lighting11< 0.1%
 
(Missing)2840.1%
 
2020-09-03T16:09:21.681666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length24
Median length8
Mean length11.79980273
Min length3

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories (?)4
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t26986512.1%
 
2048899.2%
 
D1755977.8%
 
a1699847.6%
 
h1674377.5%
 
i1672137.5%
 
g1672137.5%
 
y1159775.2%
 
l1159775.2%
 
r1026744.6%
 
e1026634.6%
 
n920054.1%
 
k706053.2%
 
s571112.6%
 
-512252.3%
 
L512252.3%
 
S512142.3%
 
O499132.2%
 
w159810.7%
 
o150190.7%
 
U134830.6%
 
u58970.3%
 
f23980.1%
 
N15360.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter163801973.2%
 
Uppercase Letter34296815.3%
 
Space Separator2048899.2%
 
Dash Punctuation512252.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D17559751.2%
 
L5122514.9%
 
S5121414.9%
 
O4991314.6%
 
U134833.9%
 
N15360.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t26986516.5%
 
a16998410.4%
 
h16743710.2%
 
i16721310.2%
 
g16721310.2%
 
y1159777.1%
 
l1159777.1%
 
r1026746.3%
 
e1026636.3%
 
n920055.6%
 
k706054.3%
 
s571113.5%
 
w159811.0%
 
o150190.9%
 
u58970.4%
 
f23980.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
204889100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-51225100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin198098788.6%
 
Common25611411.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t26986513.6%
 
D1755978.9%
 
a1699848.6%
 
h1674378.5%
 
i1672138.4%
 
g1672138.4%
 
y1159775.9%
 
l1159775.9%
 
r1026745.2%
 
e1026635.2%
 
n920054.6%
 
k706053.6%
 
s571112.9%
 
L512252.6%
 
S512142.6%
 
O499132.5%
 
w159810.8%
 
o150190.8%
 
U134830.7%
 
u58970.3%
 
f23980.1%
 
N15360.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20488980.0%
 
-5122520.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2237101100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t26986512.1%
 
2048899.2%
 
D1755977.8%
 
a1699847.6%
 
h1674377.5%
 
i1672137.5%
 
g1672137.5%
 
y1159775.2%
 
l1159775.2%
 
r1026744.6%
 
e1026634.6%
 
n920054.1%
 
k706053.2%
 
s571112.6%
 
-512252.3%
 
L512252.3%
 
S512142.3%
 
O499132.2%
 
w159810.7%
 
o150190.7%
 
U134830.6%
 
u58970.3%
 
f23980.1%
 
N15360.1%
 

PEDROWNOTGRNT
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing184954
Missing (%)97.6%
Memory size1.4 MiB
Y
4634
ValueCountFrequency (%) 
Y46342.4%
 
(Missing)18495497.6%
 
2020-09-03T16:09:22.397388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.951115049
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n36990866.1%
 
a18495433.1%
 
Y46340.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter55486299.2%
 
Uppercase Letter46340.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n36990866.7%
 
a18495433.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y4634100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin559496100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n36990866.1%
 
a18495433.1%
 
Y46340.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII559496100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n36990866.1%
 
a18495433.1%
 
Y46340.8%
 

SDOTCOLNUM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct count114747
Unique (%)> 99.9%
Missing74837
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean7968383.384545668
Minimum1007024.0
Maximum13072024.0
Zeros0
Zeros (%)0.0%
Memory size1.4 MiB
2020-09-03T16:09:22.515392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1007024
5-th percentile4169033.5
Q16039023.5
median8022004
Q310153006.5
95-th percentile12220022.5
Maximum13072024
Range12065000
Interquartile range (IQR)4113983

Descriptive statistics

Standard deviation2550991.638
Coefficient of variation (CV)0.3201391693
Kurtosis-1.092290352
Mean7968383.385
Median Absolute Deviation (MAD)2068016
Skewness0.2083924938
Sum9.143799618e+11
Variance6.507558336e+12
2020-09-03T16:09:22.583729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
41120252< 0.1%
 
41160482< 0.1%
 
41160342< 0.1%
 
112000072< 0.1%
 
50360231< 0.1%
 
40280361< 0.1%
 
70870081< 0.1%
 
120040521< 0.1%
 
101610181< 0.1%
 
120270221< 0.1%
 
50360111< 0.1%
 
103420271< 0.1%
 
50360031< 0.1%
 
102040331< 0.1%
 
60780221< 0.1%
 
102780101< 0.1%
 
60780101< 0.1%
 
70870391< 0.1%
 
72190041< 0.1%
 
102090351< 0.1%
 
60780071< 0.1%
 
51180011< 0.1%
 
112100291< 0.1%
 
63160241< 0.1%
 
82090291< 0.1%
 
Other values (114722)11472260.5%
 
(Missing)7483739.5%
 
ValueCountFrequency (%) 
10070241< 0.1%
 
31370161< 0.1%
 
32390351< 0.1%
 
40010011< 0.1%
 
40010021< 0.1%
 
40010031< 0.1%
 
40010041< 0.1%
 
40010051< 0.1%
 
40010061< 0.1%
 
40010071< 0.1%
 
ValueCountFrequency (%) 
130720241< 0.1%
 
130720231< 0.1%
 
130720221< 0.1%
 
130720211< 0.1%
 
130720201< 0.1%
 
130720191< 0.1%
 
130720181< 0.1%
 
130720171< 0.1%
 
130720161< 0.1%
 
130720151< 0.1%
 

SPEEDING
Categorical

MISSING

Distinct count1
Unique (%)< 0.1%
Missing180262
Missing (%)95.1%
Memory size1.4 MiB
Y
9326
ValueCountFrequency (%) 
Y93264.9%
 
(Missing)18026295.1%
 
2020-09-03T16:09:23.313439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.901618246
Min length1

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories (?)2
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n36052465.5%
 
a18026232.8%
 
Y93261.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter54078698.3%
 
Uppercase Letter93261.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n36052466.7%
 
a18026233.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y9326100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin550112100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n36052465.5%
 
a18026232.8%
 
Y93261.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII550112100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n36052465.5%
 
a18026232.8%
 
Y93261.7%
 

ST_COLCODE
Unsupported

REJECTED
UNSUPPORTED

Missing17
Missing (%)< 0.1%
Memory size9.2 MiB

ST_COLDESC
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct count61
Unique (%)< 0.1%
Missing20
Missing (%)< 0.1%
Memory size1.4 MiB
One parked--one moving
44421
Entering at angle
34674
From same direction - both going straight - one stopped - rear-end
25771
Fixed object
13554
From same direction - both going straight - both moving - sideswipe
12777
Other values (56)
58371
ValueCountFrequency (%) 
One parked--one moving4442123.4%
 
Entering at angle3467418.3%
 
From same direction - both going straight - one stopped - rear-end2577113.6%
 
Fixed object135547.1%
 
From same direction - both going straight - both moving - sideswipe127776.7%
 
From opposite direction - one left turn - one straight103245.4%
 
From same direction - both going straight - both moving - rear-end76294.0%
 
From same direction - all others45372.4%
 
Vehicle - Pedalcyclist45232.4%
 
From same direction - one left turn - one straight30931.6%
 
From same direction - one right turn - one straight29561.6%
 
Vehicle going straight hits pedestrian28811.5%
 
One car leaving parked position28461.5%
 
From same direction - both going straight - one stopped - sideswipe24351.3%
 
One car leaving driveway access22741.2%
 
Vehicle turning left hits pedestrian21781.1%
 
One car entering driveway access16170.9%
 
From opposite direction - all others13020.7%
 
Vehicle turning right hits pedestrian12010.6%
 
Same direction -- both turning right -- both moving -- sideswipe11840.6%
 
From opposite direction - both going straight - sideswipe10390.5%
 
Same direction -- both turning left -- both moving -- sideswipe8350.4%
 
Vehicle overturned8140.4%
 
One car entering parked position7200.4%
 
From opposite direction - both moving - head-on5900.3%
 
Other values (36)33931.8%
 
2020-09-03T16:09:24.040823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length85
Median length22
Mean length36.6587073
Min length3

Overview of Unicode Properties

Unique unicode characters49
Unique unicode categories (?)7
Unique unicode scripts (?)2
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
100128414.4%
 
e6794609.8%
 
n5455747.8%
 
o5392537.8%
 
i5056007.3%
 
t4961947.1%
 
r4284026.2%
 
-3338394.8%
 
g3321894.8%
 
a3225224.6%
 
s2456613.5%
 
d2368063.4%
 
m2029962.9%
 
h1784762.6%
 
p1628482.3%
 
c1294051.9%
 
l930001.3%
 
b901721.3%
 
F866631.2%
 
v778781.1%
 
O524590.8%
 
k491550.7%
 
E347340.5%
 
u242330.3%
 
w226990.3%
 
Other values (24)785491.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter541674577.9%
 
Space Separator100128414.4%
 
Dash Punctuation3338394.8%
 
Uppercase Letter1980142.8%
 
Other Punctuation93< 0.1%
 
Open Punctuation38< 0.1%
 
Close Punctuation38< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F8666343.8%
 
O5245926.5%
 
E3473417.5%
 
V130576.6%
 
P54112.7%
 
S37751.9%
 
A4160.2%
 
M3470.2%
 
R2750.1%
 
C2230.1%
 
N1150.1%
 
L1140.1%
 
I80< 0.1%
 
T80< 0.1%
 
D75< 0.1%
 
H62< 0.1%
 
Y60< 0.1%
 
B34< 0.1%
 
U20< 0.1%
 
W14< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e67946012.5%
 
n54557410.1%
 
o53925310.0%
 
i5056009.3%
 
t4961949.2%
 
r4284027.9%
 
g3321896.1%
 
a3225226.0%
 
s2456614.5%
 
d2368064.4%
 
m2029963.7%
 
h1784763.3%
 
p1628483.0%
 
c1294052.4%
 
l930001.7%
 
b901721.7%
 
v778781.4%
 
k491550.9%
 
u242330.4%
 
w226990.4%
 
f171130.3%
 
j139940.3%
 
x135540.3%
 
y95610.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1001284100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-333839100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(38100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,93100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)38100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin561475980.8%
 
Common133529219.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e67946012.1%
 
n5455749.7%
 
o5392539.6%
 
i5056009.0%
 
t4961948.8%
 
r4284027.6%
 
g3321895.9%
 
a3225225.7%
 
s2456614.4%
 
d2368064.2%
 
m2029963.6%
 
h1784763.2%
 
p1628482.9%
 
c1294052.3%
 
l930001.7%
 
b901721.6%
 
F866631.5%
 
v778781.4%
 
O524590.9%
 
k491550.9%
 
E347340.6%
 
u242330.4%
 
w226990.4%
 
f171130.3%
 
j139940.2%
 
Other values (19)472730.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
100128475.0%
 
-33383925.0%
 
,93< 0.1%
 
(38< 0.1%
 
)38< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6950051100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
100128414.4%
 
e6794609.8%
 
n5455747.8%
 
o5392537.8%
 
i5056007.3%
 
t4961947.1%
 
r4284026.2%
 
-3338394.8%
 
g3321894.8%
 
a3225224.6%
 
s2456613.5%
 
d2368063.4%
 
m2029962.9%
 
h1784762.6%
 
p1628482.3%
 
c1294051.9%
 
l930001.3%
 
b901721.3%
 
F866631.2%
 
v778781.1%
 
O524590.8%
 
k491550.7%
 
E347340.5%
 
u242330.3%
 
w226990.3%
 
Other values (24)785491.1%
 

SEGLANEKEY
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1865
Unique (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.7142751650948
Minimum0
Maximum525241
Zeros186973
Zeros (%)98.6%
Memory size1.4 MiB
2020-09-03T16:09:24.117852image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum525241
Range525241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3293.690652
Coefficient of variation (CV)12.63333452
Kurtosis10162.37047
Mean260.7142752
Median Absolute Deviation (MAD)0
Skewness69.22763158
Sum49428298
Variance10848398.11
2020-09-03T16:09:24.189553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018697398.6%
 
653219< 0.1%
 
607816< 0.1%
 
1216215< 0.1%
 
1033613< 0.1%
 
1034213< 0.1%
 
898512< 0.1%
 
1035410< 0.1%
 
1042010< 0.1%
 
1217910< 0.1%
 
103689< 0.1%
 
88169< 0.1%
 
107738< 0.1%
 
105908< 0.1%
 
89958< 0.1%
 
105667< 0.1%
 
129417< 0.1%
 
103747< 0.1%
 
427777< 0.1%
 
104086< 0.1%
 
63226< 0.1%
 
421666< 0.1%
 
235076< 0.1%
 
82406< 0.1%
 
126496< 0.1%
 
Other values (1840)23911.3%
 
ValueCountFrequency (%) 
018697398.6%
 
11891< 0.1%
 
12001< 0.1%
 
12481< 0.1%
 
12571< 0.1%
 
12711< 0.1%
 
13091< 0.1%
 
13501< 0.1%
 
13711< 0.1%
 
14081< 0.1%
 
ValueCountFrequency (%) 
5252411< 0.1%
 
5251691< 0.1%
 
5211171< 0.1%
 
592601< 0.1%
 
547281< 0.1%
 
469811< 0.1%
 
458321< 0.1%
 
458311< 0.1%
 
458001< 0.1%
 
457481< 0.1%
 

CROSSWALKKEY
Real number (ℝ≥0)

ZEROS

Distinct count2162
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9800.749192986897
Minimum0
Maximum5239700
Zeros185866
Zeros (%)98.0%
Memory size1.4 MiB
2020-09-03T16:09:24.269592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5239700
Range5239700
Interquartile range (IQR)0

Descriptive statistics

Standard deviation72355.21141
Coefficient of variation (CV)7.382620449
Kurtosis191.3942744
Mean9800.749193
Median Absolute Deviation (MAD)0
Skewness8.911590414
Sum1858104438
Variance5235276618
2020-09-03T16:09:24.344310image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
018586698.0%
 
52360917< 0.1%
 
52083815< 0.1%
 
52556713< 0.1%
 
52170710< 0.1%
 
5236999< 0.1%
 
5216049< 0.1%
 
5237359< 0.1%
 
5242659< 0.1%
 
5228919< 0.1%
 
5240298< 0.1%
 
5222648< 0.1%
 
5210408< 0.1%
 
5221088< 0.1%
 
5218638< 0.1%
 
5256448< 0.1%
 
5231098< 0.1%
 
5246898< 0.1%
 
5239878< 0.1%
 
5223778< 0.1%
 
5208558< 0.1%
 
5231488< 0.1%
 
5241788< 0.1%
 
5215747< 0.1%
 
5231767< 0.1%
 
Other values (2137)35041.8%
 
ValueCountFrequency (%) 
018586698.0%
 
5231< 0.1%
 
73581< 0.1%
 
90731< 0.1%
 
105901< 0.1%
 
154851< 0.1%
 
175581< 0.1%
 
212141< 0.1%
 
238601< 0.1%
 
238781< 0.1%
 
ValueCountFrequency (%) 
52397001< 0.1%
 
7034801< 0.1%
 
7013061< 0.1%
 
7012801< 0.1%
 
7011101< 0.1%
 
7005261< 0.1%
 
7003881< 0.1%
 
6998891< 0.1%
 
6998791< 0.1%
 
6998761< 0.1%
 

HITPARKEDCAR
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
N
182670
Y
 
6918
ValueCountFrequency (%) 
N18267096.4%
 
Y69183.6%
 

Interactions

2020-09-03T16:08:34.008408image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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Correlations

2020-09-03T16:09:24.443611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-03T16:09:24.627401image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-03T16:09:24.809787image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-03T16:09:25.015960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-03T16:09:25.259857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-03T16:09:00.469176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-03T16:09:02.387859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-03T16:09:03.765182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-03T16:09:04.283710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexSEVERITYCODEXYOBJECTIDINCKEYCOLDETKEYREPORTNOSTATUSADDRTYPEINTKEYLOCATIONEXCEPTRSNCODEEXCEPTRSNDESCSEVERITYCODE.1SEVERITYDESCCOLLISIONTYPEPERSONCOUNTPEDCOUNTPEDCYLCOUNTVEHCOUNTINCDATEINCDTTMJUNCTIONTYPESDOT_COLCODESDOT_COLDESCINATTENTIONINDUNDERINFLWEATHERROADCONDLIGHTCONDPEDROWNOTGRNTSDOTCOLNUMSPEEDINGST_COLCODEST_COLDESCSEGLANEKEYCROSSWALKKEYHITPARKEDCAR
002-122.32314847.7031401130713073502005MatchedIntersection37475.05TH AVE NE AND NE 103RD STNaN2Injury CollisionAngles20022013/03/27 00:00:00+003/27/2013 2:54:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNOvercastWetDaylightNaNNaNNaN10Entering at angle00N
111-122.34729447.647172252200522002607959MatchedBlockNaNAURORA BR BETWEEN RAYE ST AND BRIDGE WAY NNaNNaN1Property Damage Only CollisionSideswipe20022006/12/20 00:00:00+0012/20/2006 6:55:00 PMMid-Block (not related to intersection)16MOTOR VEHICLE STRUCK MOTOR VEHICLE, LEFT SIDE SIDESWIPENaN0RainingWetDark - Street Lights OnNaN6354039.0NaN11From same direction - both going straight - both moving - sideswipe00N
221-122.33454047.607871326700267001482393MatchedBlockNaN4TH AVE BETWEEN SENECA ST AND UNIVERSITY STNaNNaN1Property Damage Only CollisionParked Car40032004/11/18 00:00:00+0011/18/2004 10:20:00 AMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDNaN0OvercastDryDaylightNaN4323031.0NaN32One parked--one moving00N
331-122.33480347.6048034114411443503937MatchedBlockNaN2ND AVE BETWEEN MARION ST AND MADISON STNaN1Property Damage Only CollisionOther30032013/03/29 00:00:00+003/29/2013 9:26:00 AMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN23From same direction - all others00N
442-122.30642647.545739517700177001807429MatchedIntersection34387.0SWIFT AVE S AND SWIFT AV OFF RPNaNNaN2Injury CollisionAngles20022004/01/28 00:00:00+001/28/2004 8:04:00 AMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0RainingWetDaylightNaN4028032.0NaN10Entering at angle00N
551-122.38759847.6905756320840322340E919477MatchedIntersection36974.024TH AVE NW AND NW 85TH STNaN1Property Damage Only CollisionAngles20022019/04/20 00:00:00+004/20/2019 5:42:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
661-122.33848547.618534783300833003282542MatchedIntersection29510.0DENNY WAY AND WESTLAKE AVENaNNaN1Property Damage Only CollisionAngles20022008/12/09 00:00:00+0012/9/2008At Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0RainingWetDaylightNaN8344002.0NaN10Entering at angle00N
772-122.32078047.6140769330897332397EA30304MatchedIntersection29745.0BROADWAY AND E PIKE STNaN2Injury CollisionCycles30112020/04/15 00:00:00+004/15/2020 5:47:00 PMAt Intersection (intersection related)51PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN5Vehicle Strikes Pedalcyclist68550N
881-122.33593047.6119041063400634002071243MatchedBlockNaNPINE ST BETWEEN 5TH AVE AND 6TH AVENaNNaN1Property Damage Only CollisionParked Car20022006/06/15 00:00:00+006/15/2006 1:00:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0ClearDryDaylightNaN6166014.0NaN32One parked--one moving00N
992-122.38470047.5284751258600586002072105MatchedIntersection34679.041ST AVE SW AND SW THISTLE STNaNNaN2Injury CollisionAngles20022006/03/20 00:00:00+003/20/2006 3:49:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaN0ClearDryDaylightNaN6079001.0NaN10Entering at angle00N

Last rows

df_indexSEVERITYCODEXYOBJECTIDINCKEYCOLDETKEYREPORTNOSTATUSADDRTYPEINTKEYLOCATIONEXCEPTRSNCODEEXCEPTRSNDESCSEVERITYCODE.1SEVERITYDESCCOLLISIONTYPEPERSONCOUNTPEDCOUNTPEDCYLCOUNTVEHCOUNTINCDATEINCDTTMJUNCTIONTYPESDOT_COLCODESDOT_COLDESCINATTENTIONINDUNDERINFLWEATHERROADCONDLIGHTCONDPEDROWNOTGRNTSDOTCOLNUMSPEEDINGST_COLCODEST_COLDESCSEGLANEKEYCROSSWALKKEYHITPARKEDCAR
1895781946632-122.29916047.579673219536309335310615E880807MatchedBlockNaNRAINIER AVE S BETWEEN S BAYVIEW ST AND S MCCLELLAN STNaN2Injury CollisionAngles30022019/01/09 00:00:00+001/9/2019 12:51:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLEYNRainingWetDaylightNaNNaNNaN10Entering at angle00N
1895791946641-122.32588747.643191219537309222310502E879537MatchedIntersection28300.0EASTLAKE AVE E AND E ROANOKE STNaN1Property Damage Only CollisionAngles80032018/12/30 00:00:00+0012/30/2018 3:25:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
1895801946651-122.30421747.6695372195383084803097603642620MatchedIntersection26005.0NE PARK RD AND NE RAVENNA WB BVNaN1Property Damage Only CollisionAngles20022018/12/05 00:00:00+0012/5/2018 1:00:00 PMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN10Entering at angle00N
1895811946662-122.34456947.694547219539309170310450E879712MatchedBlockNaNAURORA AVE N BETWEEN N 90TH ST AND N 91ST STNaN2Injury CollisionAngles20022019/01/04 00:00:00+001/4/2019 1:46:00 PMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearWetDaylightNaNNaNNaN10Entering at angle00N
1895821946671-122.36167247.5567222195413078043090843745813MatchedBlockNaNPUGET BLVD SW BETWEEN SW HUDSON ST AND DEAD END 1NaN1Property Damage Only CollisionOther10012018/11/28 00:00:00+0011/28/2018 9:34:00 PMMid-Block (not related to intersection)28MOTOR VEHICLE RAN OFF ROAD - HIT FIXED OBJECTNaNYRainingWetDark - Street Lights OnNaNNaNNaN50Fixed object00N
1895831946682-122.29082647.565408219543309534310814E871089MatchedBlockNaN34TH AVE S BETWEEN S DAKOTA ST AND S GENESEE STNaN2Injury CollisionHead On30022018/11/12 00:00:00+0011/12/2018 8:12:00 AMMid-Block (not related to intersection)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN24From opposite direction - both moving - head-on00N
1895841946691-122.34452647.690924219544309085310365E876731MatchedBlockNaNAURORA AVE N BETWEEN N 85TH ST AND N 86TH STNaN1Property Damage Only CollisionRear Ended20022018/12/18 00:00:00+0012/18/2018 9:14:00 AMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDYNRainingWetDaylightNaNNaNNaN13From same direction - both going straight - both moving - rear-end00N
1895851946702-122.30668947.6830472195453112803126403809984MatchedIntersection24760.020TH AVE NE AND NE 75TH STNaN2Injury CollisionLeft Turn30022019/01/19 00:00:00+001/19/2019 9:25:00 AMAt Intersection (intersection related)11MOTOR VEHICLE STRUCK MOTOR VEHICLE, FRONT END AT ANGLENaNNClearDryDaylightNaNNaNNaN28From opposite direction - one left turn - one straight00N
1895861946712-122.35531747.6787342195463095143107943810083MatchedIntersection24349.0GREENWOOD AVE N AND N 68TH STNaN2Injury CollisionCycles20112019/01/15 00:00:00+001/15/2019 4:48:00 PMAt Intersection (intersection related)51PEDALCYCLIST STRUCK MOTOR VEHICLE FRONT END AT ANGLENaNNClearDryDuskNaNNaNNaN5Vehicle Strikes Pedalcyclist43080N
1895871946721-122.28936047.611017219547308220309500E868008MatchedBlockNaN34TH AVE BETWEEN E MARION ST AND E SPRING STNaN1Property Damage Only CollisionRear Ended20022018/11/30 00:00:00+0011/30/2018 3:45:00 PMMid-Block (not related to intersection)14MOTOR VEHICLE STRUCK MOTOR VEHICLE, REAR ENDNaNNClearWetDaylightNaNNaNNaN14From same direction - both going straight - one stopped - rear-end00N